scholarly journals Toward a Standardized Encoding of Remote Sensing Geo-Positioning Sensor Models

2020 ◽  
Vol 12 (9) ◽  
pp. 1530
Author(s):  
Meng Jin ◽  
Yuqi Bai ◽  
Emmanuel Devys ◽  
Liping Di
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Image Data ◽  
Xml Schema ◽  
Data Interoperability ◽  
Sensing Data ◽  
Sensor Model ◽  
Application Systems ◽  
Extensible Markup ◽  
Sensor Models

Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain.

scholarly journals A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB

2019 ◽  
Vol 8 (12) ◽  
pp. 533 ◽  
Author(s):  
Shuang Wang ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Yi Zeng ◽  
Lushen Pang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Relational Databases ◽  
Large Scale ◽  
Distributed Storage ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Image Data ◽  
Sensing Data ◽  
Cluster Architecture ◽  
Distributed Cluster

With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data.

scholarly journals Urban Classification Using Multi-temporal Sentinel-1 Data Based On Coherence Characteristics

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Le Minh Hang ◽  
Tran Anh Tuan
Keyword(s):  
Remote Sensing ◽  
European Space Agency ◽  
Remote Sensing Data ◽  
Image Data ◽  
Development Planning ◽  
Optical Remote Sensing ◽  
Satellite Mission ◽  
Sar Images ◽  
Sensing Data ◽  
Polarization Image

Classification urban features plays an important part in monitoring and development planning of the area. Optical remote sensing data is currently used in study land use/land cover. However, optical remote sensing data are affected by clouds and weather. Hence, it is difficult to update information. Sentinel-1 is the satellite mission which conducted by the European Space Agency (ESA). Sentinel-1 is composed of two satellites, Sentinel-1A and Sentinel-1B which carried C-band Synthetic Aperture Radar (SAR) instrument, 10m spatial resolution and provided free of charge. SAR images, which is an active microwave data, is not affected by weather, day and night. In this article, the authors present the experimental results of using coherence technique of two SAR images acquised at different times to classify urban features. The classification accuracy by using VV and VH polarization images were respectively 89% and 93%. VH polarization image data used in classification urban feature is better than VV polarization image.

scholarly journals AN ENHANCED ALGORITHM FOR AUTOMATIC RADIOMETRIC HARMONIZATION OF HIGH-RESOLUTION OPTICAL SATELLITE IMAGERY USING PSEUDOINVARIANT FEATURES AND LINEAR REGRESSION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 115-120
Author(s):  
M. Langheinrich ◽  
P. Fischer ◽  
M. Probeck ◽  
G. Ramminger ◽  
T. Wagner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Multispectral Imaging ◽  
Satellite Image ◽  
Spectral Composition ◽  
Remote Sensing Data ◽  
Image Data ◽  
Optical Remote Sensing ◽  
Surface Reflection ◽  
Sensing Data

The growing number of available optical remote sensing data providing large spatial and temporal coverage enables the coherent and gapless observation of the earth’s surface on the scale of whole countries or continents. To produce datasets of that size, individual satellite scenes have to be stitched together forming so-called mosaics. Here the problem arises that the different images feature varying radiometric properties depending on the momentary acquisition conditions. The interpretation of optical remote sensing data is to a great extent based on the analysis of the spectral composition of an observed surface reflection. Therefore the normalization of all images included in a large image mosaic is necessary to ensure consistent results concerning the application of procedures to the whole dataset. In this work an algorithm is described which enables the automated spectral harmonization of satellite images to a reference scene. As the stable and satisfying functionality of the proposed algorithm was already put to operational use to process a high number of SPOT-4/-5, IRS LISS-III and Landsat-5 scenes in the frame of the European Environment Agency's Copernicus/GMES Initial Operations (GIO) High-Resolution Layer (HRL) mapping of the HRL Forest for 20 Western, Central and (South)Eastern European countries, it is further evaluated on its reliability concerning the application to newer Sentinel-2 multispectral imaging products. The results show that the algorithm is comparably efficient for the processing of satellite image data from sources other than the sensor configurations it was originally designed for.

scholarly journals AN APPROACH FOR EVALUATING THE INFORMATION CONTENT OF REMOTE SENSING IMAGES

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 21-26
Author(s):  
S. M. Fang ◽  
X. G. Zhou
Keyword(s):  
Remote Sensing ◽  
Information Content ◽  
Evaluation Method ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Image Data ◽  
Structural Features ◽  
Utilization Efficiency ◽  
Sensing Data

Abstract. Due to being affected by the rapid development of open science and the increasing popularity of mobile devices (e.g., smartphones), remote sensing data as frequently used data sources are broadly applied to our daily life. At the same time, remote sensing data collection also presents a trend of popularization. To improve the utilization efficiency and availability of the obtained diversified remote sensing data, we propose a novel evaluation method based on information theory and scatterplot mapping model, i.e., geometrical mapping entropy (GME). The goal is to construct a unified model of measurement to be much more effectively and accurately evaluate the information content and quality of remotely sensed imagery. Different experimental data are used to verify the performance of the proposed method, i.e., a group of the dataset that contains different four types of images; the other group of image data contains the images with different modalities and different imaging times (2016–05, 2017–08, 2018–04, and 2018–06). Experimental results indicate that the proposed approach can better characterize the spectrum features and spatial structural features contained in images and visual perception information. Additionally, it can also reflect the difference in the quality of different modality images, especially the effect for the images that contain clouds or poor lighting conditions, is better.

scholarly journals Estimation of debris cover and its temporal variation using optical satellite sensor data: a case study in Chenab basin, Himalaya

2009 ◽  
Vol 55 (191) ◽  
pp. 444-452 ◽  
Author(s):  
A. Shukla ◽  
R.P. Gupta ◽  
M.K. Arora
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Image Data ◽  
Optical Remote Sensing ◽  
Glacier Area ◽  
Sensing Data ◽  
Debris Cover ◽  
Topographical Maps

AbstractDebris cover over glaciers greatly affects their rate of ablation and is a sensitive indicator of glacier health. This study focuses on estimation of debris cover over Samudratapu glacier, Chenab basin, Himalaya, using optical remote-sensing data. Remote-sensing image data of IRS-1C LISS-III (September 2001), IRS-P6 AWiFS (September 2004) and Terra ASTER (September 2004) along with Survey of India topographical maps (1963) were used in the study. Supervised classification of topographically corrected reflectance image data was systematically conducted to map six land-cover classes in the glacier terrain: snow, ice, mixed ice and debris, debris, valley rock, and water. An accuracy assessment of the classification was conducted using the ASTER visible/near-infrared data as the reference. The overall accuracies of the glacier-cover maps were found to range from 83.7% to 89.1%, whereas the individual class accuracy of debris-cover mapping was found to range from 82% to 95%. This shows that supervised classification of topographically corrected reflectance data is effective for the extraction of debris cover. In addition, a comparative study of glacier-cover maps generated from remote-sensing data (supervised classification) of September 2001 and September 2004 and Survey of India topographical maps (1963) has highlighted the trends of glacier depletion and recession. The glacier snout receded by about 756 m from 1963 to 2004, and the total glacier area was reduced by 13.7 km2 (from 110 km2 in 1963). Further, glacier retreat is found to be accompanied by a decrease in mixed ice and debris and a marked increase in debris-cover area. The area covered by valley rock is found to increase, confirming an overall decrease in the glacier area. The results from this study demonstrate the applicability of optical remote-sensing data in monitoring glacier terrain, and particularly mapping debris-cover area.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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